Print Email Facebook Twitter Automated detection of performance regressions in web applications using association rule mining Title Automated detection of performance regressions in web applications using association rule mining Author Zaleznicenka, Z. Contributor Zaidman, A.E. (mentor) Faculty Electrical Engineering, Mathematics and Computer Science Department Software and Computer Technology Programme Computer Science Date 2013-11-20 Abstract Performance testing is an important stage of developing web applications intended to operate with high availability under severe load. However, this process still remains to a large extent elaborate, expensive and unreliable. Most often the performance testing activities are being done manually, and this significantly affects development time and costs. This thesis report describes an approach aimed at automating the analysis of performance tests by maintaining a repository with the results of previously completed test runs and comparing them with the new runs to reveal deviations in software performance behaviour. Detection of performance degradations is executed in a fast way using well-known data mining techniques. The results of conducted case studies clearly indicate that the suggested approach may successfully assist software engineers with detecting performance regressions in the evolving software. Subject performance testingsoftware engineeringregression detectionassociation rule mining To reference this document use: http://resolver.tudelft.nl/uuid:076ec1f6-d6c6-4266-afc5-dd71e24d65b2 Embargo date 2013-11-06 Part of collection Student theses Document type master thesis Rights (c) 2013 Zaleznicenka, Z. Files PDF zaleznicenka_msc_thesis.pdf 1.82 MB Close viewer /islandora/object/uuid:076ec1f6-d6c6-4266-afc5-dd71e24d65b2/datastream/OBJ/view